Text-to-Audio Generation

Text-to-audio generation studies how to turn written prompts into speech, music, sound effects, and layered soundscapes. Common approaches use latent diffusion or flow matching, often with semantic audio representations; autoregressive models and language-model planning are also used to organize events and support longer or more complex outputs. Researchers are working to improve prompt adherence, temporal order, and control of specific sounds, while evaluation increasingly examines individual events and attributes rather than relying only on global similarity scores. A central challenge is balancing faithful, controllable generation with diversity, high audio quality, and efficient inference.